Shima Nasiri; Saman Abdanan; Mokhtar Heidari
Abstract
Introduction: Texture represents one of the four principal factors defining food/fruit quality, together with appearance, flavour and nutritional properties (Bourne, 2002), and plays a key role in consumer acceptability and recognition of quince. Textural characteristics of quinces defined by “crispness”, ...
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Introduction: Texture represents one of the four principal factors defining food/fruit quality, together with appearance, flavour and nutritional properties (Bourne, 2002), and plays a key role in consumer acceptability and recognition of quince. Textural characteristics of quinces defined by “crispness”, “juiciness”, “hardness”,“firmness” and “mealiness” are often key drivers of consumer preference. Many non-destructive methods, including image analysis, spectroscopy, ultrasound and sound techniques, have been developed to diagnose internal and external defects in fruits and vegetables. Cheng and Haugh (1994) used a frequency of 250-kHz, rather than 1-MHz, to detect hollow heart. They were not able to transmit successfully the ultrasound wave through the whole tuber using 1-MHz transducers but found the 250-kHz transducers to be practical for a transmission path length of up to 89.7 mm. In a research an acoustic setup was developed to simultaneously detect the resonant frequencies from equator and from calyx shoulder of pear. The researchers proposed index based on these two frequencies was used for firmness evaluation of non-spherical pear; Compared with two types of single frequency-based indices, the firmness sensitivity of the dual-frequency index is mostly close to that of MT penetration test. The firmness index can classify pears with a high total accuracy (93.4%), making it suitable for nondestructive detection of firmness of differently shaped pears (Zhang et al., 2018). The goal of this study was to develop a nondestructive method based on acoustic impulse response of quince fruit using genetic programming and artificial neural network during storage. Materials and Methods: In the experiment 120 quince fruits (Cydonia oblonga) were harvested from a field near Isfahan 181 days after full flowering of the trees. For each cultivar, only samples of similar size and without visible external damage were chosen. The samples were packed in sterile nylon bags and stored at 4°C. Non-destructive test (acoustic response) as well as destructive test (chemical measurement and penetration test) were performed every 15 days for 4 months (Akbari Bisheh et al., 2014). Total soluble solids (TSS) were determined by a hand refractometer device (model: MT03 Japan) and expressed as °Brix. Ascorbic acid of the juice was measured by titration with copper sulfate and potassium iodide based on the Barakat et al. (1973) procedure. Titratable acidity was measured according to the AOAC method. To determine the total phenol content of juice, the Waterhouse method (2000) was used. Determination of the pH of the fruit extract using a pH meter (Portable Model P-755, Japan). Physical attributes of the samples including volume as well as major, minor, intermittent diameters and mass were calculated using the relations proposed by Stroshine and Hammand (1994). Penetration test was conducted by the material test machine (SANTAM, STM-20 model, Iran).In order to analyze the response sound signal of quince in time and frequency domain, a system equipped with a sample holder with foam rubber covered surface, an impact mechanism, a microphone and an electronic circuit was utilized. To record impact sound features a microphone was positioned next to the fruit and was hit at three speed level (0.3, 0.9 and 1.5 m/s). After recoding sound, five features (acoustic peak, maximum acoustic pressure, mean acoustic pressure and natural frequency) were extracted and used as inputs for models. In order to predict the stiffness, four methods of genetic programming, neural network and existing mathematical models (FI and SIQ-FT) were used. In order to carry out statistical analysis, analysis of variance (ANOVA) and Duncan's multiple range test at 5% probability level were performed according to the completely randomized design (CRD). Results and discussion: In this study, Duncan's multiple range comparison test was used to investigate the significant difference between destructive and non-destructive parameters at 5% probability level. According to the results, acoustic peak, maximum acoustic pressure, mean acoustic pressure and natural frequency were decreased by increasing storage time. Statistical analysis of the destructive tests also showed a decreasing trend at the 5% level. In several papers, two mathematical equations have been used to obtain the relationship between the mass resonance frequency and the sound of impact. In this study, genetic programming and neural network modeling were used to compare the results of these relationships. The regression coefficients between the actual and the predicted values for the resonance-mass relation and the effect of the sound from the collision were R2= 0.601 and R2= 0.754, respectively. Also, the regression values obtained from genetic programming and neural network modeling were R2= 0.9567 and R2 = 0.933, respectively. In a research, the overall R2 value amounts for stiffness prediction was reported to be 0.79 (Schotte et al., 1999). Abbaszadeh et al. (2013) evaluated watermelons texture using their vibration responses. They declared their proposed method could predict textural acceptability of watermelons with determination coefficients 0.99. According to the obtained values, the best methods for stiffness prediction were genetic programming and f neural network methods, respectively.
Amir Gitiban; Narmela Asefi
Abstract
Introduction: Dried fruits are one of the most important non-oil exports and the efforts should be made to grow the economy of the country by increasing their exports to world markets. Meanwhile, quince juice contains various minerals including iron, phosphorus, calcium, potassium and rich in vitamins ...
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Introduction: Dried fruits are one of the most important non-oil exports and the efforts should be made to grow the economy of the country by increasing their exports to world markets. Meanwhile, quince juice contains various minerals including iron, phosphorus, calcium, potassium and rich in vitamins such as vitamins A, C and B vitamins. Drying of food is one of the ways to keep its quality and increase its shelflife. During this process, the removal of moisture through the simultaneous transfer of heat and mass occurs. By transferring heat from the environment to the foodstuff, the heat energy evaporates the surface moisture. The drying process has a great impact on the product. In recent years, new and innovative techniques have been considered that increase the drying rate and maintain the quality of the product and infrared drying is one of these novel techniques.. Infrared systems are emitting electromagnetic waves with a wavelength of 700 nm to 1 mm. The advantage of using infrared is to minimize waste and prevent product quality loss due to reduced drying time can be mentioned. The need to predict product quality in each process makes it necesary to model and discover the relationship between factors that can affect the final quality of the product. Artificial neural networks have been considered as a meta-innovative algorithm for modeling and prediction, which can be favored by the ability of these networks to model and predict processes The complexity and discovery of non-random fluctuations in data and the ability to discover the interactions between variables, economical savings in the use and disconnection of classical model abusive constraints (Togrul et al., 2004), the ability to reduce The effect of non-effective variables on the model by setting internal parameters is the ability to predict the desired parameter variations with minimum parameters (Bowers et al., 2000). Materials and methods: In this research, quince fruit (Variety of Isfahan) was purchased as the premium product of Isfahan Gardens and was kept at 0°C in the cold room prior to further experiments. The fruits were removed from the refrigerator one hour before processing and exposed to ambient temperature. After washing, surface moisture was removed by moisture absorbent paper and turned into slices with a constant thickness of 4 mm. The specimens were subjected to pre-treatment with an osmotic solution (vacuum for 70 minutes at a temperature of 40 ° C for 5 hours). For drying the samples, an infrared convective dryer with three voltages (800.400 and 1200 watts) and a constant speed of 0.5 m / s was used. In this way, the samples were placed under infrared lamps on a plate made from a grid and the weight of the samples was measured in a scale of 10 minutes by means of a scale and recorded on the computer. In order to achieve stable conditions in the system, the dryer was switched on 30 minutes before the process. The distance between the samples and the infrared lamp was fixed in all treatments at 16 cm. The drying process continued to reach a moisture content of 0.22 basis. To perform a puncture tests, quince slices were used in a Brookfield-based American LFRA-4500 tissue analysis device. In order to model these parameters in the drying process, the results of examining the quality of the samples, including the firmness of the tissue as well as the drying time, were used as network outputs. The power, concentration and pressure parameters were considered as network inputs. In this research, a multilayer perceptron network (MLP) was used. Due to its simplicity and high precision, this model has a great application in modeling the drying of agricultural products. Many functions in transmitting numbers from the previous layer to the next layer may be used (Tripathy et al., 2008). Result & discussion: The results indicated that the stiffness of the tissue is reduced in vacuum conditions with increased power. So, the least amount of stiffness was related to osmotic sample dried at 1200 watts. By increasing the infrared power, the stiffness of the tissue decreases, the reason for this is probably the volume increase phenomenon that occurs during the rapid evaporation of moisture through infrared rays from inside the tissue. The results showed that at the start of the drying process, due to the high moisture content of the product, the moisture loss rate is high. Gradually, with the advent of time and reduced initial moisture content, the rate of moisture reduction naturally decreases. At lower power, the drying time is longer and with increasing power, the drying time decreases due to the increase of the thermal gradient inside the product and consequently the increase in the rate of evaporation of the moisture content of the product. The results of this study showed that the neural artificial network, as a powerful tool, can estimate the stiffness parameters of the tissue and the drying time with high precision. The most suitable neural network structure to predict these parameters with a 3-7-2 topology along with logarithmic activation functions with a total explanation coefficient above 0.9923 represent the best results. Also, by increasing the drying capacity and using osmotic dehydration, the drying time and the stiffness of the tissue samples is decreased.
